System for Improving Shape-Based Targeting By Using Interest Level Data

ABSTRACT

A system for improving shape-based targeting by using interest level data is disclosed. According to one embodiment, a computer-implemented method comprises creating one or more trade zones, wherein creating a trade zone comprises grouping a set of parameters to deliver custom shapes, clustering the custom shapes according to offline data and geographic distribution of IP addresses, and mapping clusters of the custom shapes to IP addresses. Data indicating consumption of a content source is received, the content source having a plurality of tokens. The plurality of tokens is organized according to the one or more trade zones, wherein the one or more trade zones consume the tokens at a calculated rate and the calculated rate is analyzed to determine an interest associated with each trade zone. Targeting is based on a selected trade zone, wherein the selected trade zone is selected based upon a desired interest representative of a desired audience. A targeting request is transmitted including instructions or information associated with the target action, and the target action is performed.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation-in-part of U.S. patentapplication Ser. No. 12/789,731, entitled “SYSTEM FOR HANDLING MULTIPLESIMULTANEOUS CAMPAIGNS THAT IMPROVES ADVERTISEMENT PERFORMANCE THROUGHSHAPE BASED TARGETING AND REAL-TIME IMPRESSION ACQUISITION,” filed onMay 28, 2010, the disclosure of which is hereby incorporated byreference in its entirety.

FIELD

The field of the invention relates generally to computer systems. Inparticular, the present invention is directed to a system for improvingshape-based targeting by using interest level data.

BACKGROUND

Ad serving describes the technology and service that placesadvertisements on web sites. Ad serving technology companies providesoftware and/or services to web sites and advertisers to serve ads,count them, choose the ads that will make the website the most money orthe advertiser the best return, and monitor progress of differentadvertising campaigns.

Ad servers come in two flavors: local ad servers and third-party orremote ad servers. Local ad servers are typically run by a singlepublisher and serve ads to that publisher's domains, allowingfine-grained creative, formatting, and content control by thatpublisher. Remote ad servers can serve ads across domains owned bymultiple publishers. They deliver the ads from one central source sothat advertisers and publishers can track the distribution of theironline advertisements, and have one location for controlling therotation and distribution of their advertisements across the web.

Behavioral targeting is currently implemented in the art by usingcookies to track the pages that a browser visits on the Internet andusing this information to place the browser user into defined segments.These segment assignments are then used to target ads to that browser.This requires that the browser has a cookie and has been seen onlinebefore the ad call is made. Since the browser has to be seen online atsome point before the ad request, behavioral targeting is limited by thenumber of sites that participate in the page tracking.

Contextual targeting is currently implemented in the art by passingkeywords to the ad server indicating the topic of the page the ad is todisplay on. In some cases the ad server system may have analyzed thepage content beforehand and assigned it keywords based on that analysis.The ad server uses this information to select an ad that containsmatching keywords. This requires that each ad placement is setup withkeywords and that each ad has keywords that will match. Contextualtargeting is limited because it is targeting based on a single page'scontext. Contextual targeting is not able to select its audience usingparameters beyond general content and page keywords of the current page.

SUMMARY

A system for improving shape-based targeting by using interest leveldata is disclosed. According to one embodiment, a computer-implementedmethod comprises creating one or more trade zones, wherein creating atrade zone comprises grouping a set of parameters to deliver customshapes, clustering the custom shapes according to offline data andgeographic distribution of IP addresses, and mapping clusters of thecustom shapes to IP addresses. Data indicating consumption of a contentsource is received, the content source having a plurality of tokens. Theplurality of tokens is organized according to the one or more tradezones, wherein the one or more trade zones consume the tokens at acalculated rate and the calculated rate is analyzed to determine aninterest associated with each trade zone. Targeting is based on aselected trade zone, wherein the selected trade zone is selected basedupon a desired interest representative of a desired audience. Atargeting request is transmitted including instructions or informationassociated with the target action, and the target action is performed.

The above and other preferred features, including various novel detailsof implementation and combination of elements, will now be moreparticularly described with reference to the accompanying drawings andpointed out in the claims. It will be understood that the particularmethods and implementations described herein are shown by way ofillustration only and not as limitations. As will be understood by thoseskilled in the art, the principles and features described herein may beemployed in various and numerous embodiments without departing from thescope of the invention.

BRIEF DESCRIPTION

The accompanying drawings, which are included as part of the presentspecification, illustrate the presently preferred embodiment andtogether with the general description given above and the detaileddescription of the preferred embodiment given below serve to explain andteach the principles of the present invention.

FIG. 1 illustrates an exemplary computer architecture for use with thepresent system, according to one embodiment;

FIG. 2A illustrates an exemplary system level architecture for use withthe present system, according to one embodiment;

FIG. 2B illustrates an exemplary auction process for use with thepresent system;

FIG. 3 illustrates an exemplary provider system layout for use with thepresent system, according to one embodiment;

FIG. 4 illustrates an exemplary real-time bidding (RTB) bidder systemfor use with the present system, according to one embodiment;

FIG. 5 illustrates an exemplary bidder for use with the present system,according to one embodiment;

FIG. 6 illustrates an exemplary advertisement decision system for usewith the present system, according to one embodiment;

FIG. 7 illustrates an exemplary configuration process for use with thepresent system, according to one embodiment;

FIG. 8A illustrates an exemplary bidding process for use with thepresent system, according to one embodiment;

FIG. 8B illustrates an exemplary publisher targeting process for usewith the present system, according to one embodiment;

FIG. 9A illustrates an exemplary trade zone level interest determinationprocess for use with the present system, according to one embodiment;and

FIG. 9B illustrates an exemplary trade zone level interest determinationprocess for use with the present system, according to one embodiment.

It should be noted that the figures are not necessarily drawn to scaleand that elements of similar structures or functions are generallyrepresented by like reference numerals for illustrative purposesthroughout the figures. It also should be noted that the figures areonly intended to facilitate the description of the various embodimentsdescribed herein. The figures do not describe every aspect of theteachings described herein and do not limit the scope of the claims.

DETAILED DESCRIPTION

A system for improving shape-based targeting by using interest leveldata is disclosed. According to one embodiment, a computer-implementedmethod comprises creating one or more trade zones, wherein creating atrade zone comprises grouping a set of parameters to deliver customshapes, clustering the custom shapes according to offline data andgeographic distribution of IP addresses, and mapping clusters of thecustom shapes to IP addresses. Data indicating consumption of a contentsource is received, the content source having a plurality of tokens. Theplurality of tokens is organized according to the one or more tradezones, wherein the one or more trade zones consume the tokens at acalculated rate and the calculated rate is analyzed to determine aninterest associated with each trade zone. Targeting is based on aselected trade zone, wherein the selected trade zone is selected basedupon a desired interest representative of a desired audience. Atargeting request is transmitted including instructions or informationassociated with the target action, and the target action is performed.

According to one embodiment, a user interface (UI) is provided to allowfor easy use of the present system. The UI includes mechanical servingsetup for ad campaigns or other target actions as well as targetingparameters for internal and external users.

Targeting, according to one embodiment of the present system, uses ashape projection into the Internet. Custom defined shapes (e.g., tradezones) have associated data sets that indicate various characteristicsabout the audiences in those shapes. Requests are targeted to audiencesbased on these characteristics. Unlike prior art systems, the user doesnot have to be seen before and page keywords are not required to target.This allows the present system to target users who have not been seenonline before the targeting request and still have an audience match forthe request based on one or more of the applied data sets.

According to one embodiment, the present system bridges online andoffline data by mapping all the data to custom defined (trade zone)shapes. For online data the mapping uses the IP address to map to atrade zone shape. For offline data the mapping uses location informationin the offline data set to map to a trade zone shape. Examples of onlinedata include but are not limited to clicks, impressions, and pagesvisited. Examples of offline data include but are not limited todemographic data, point of sale data, and business information data.

The present system collects data about the types of content that areconsumed and aggregates that data to discern specific, elevated levelsof interests that online audiences share. The present system alsoorganizes interest level data into custom developed geographic “tradezones,” so that the data can be used to determine which audiencegroupings possess sufficient interest levels for a request to target tothat trade zone.

The present system enables target actions to reach relevant audiencesthat connect with their interests, and in so doing achieves accurate,granular, timely delivery of targeted actions. Because the data isorganized into custom trade zones, a browser does not need to have beenseen in order to be targeted, which addresses the volume challenge ofonline targeting.

The present system collects data, and analyzes it very quickly toidentify emerging interest trends or bursts of interest that wouldotherwise not be picked up by traditional market research methods in waythat would be timely (to identify emerging trends quickly) or effective(if the interest has a short cycle). According to one embodiment,consumer interests are determined in a way that does not rely ondemographic analysis, behavioral targeting, or contextual targeting. Themethod does not require cookies, or any other form of one-to-onemarketing that has raised concerns about online privacy. The presentsystem, according to one embodiment, determines the topics of interest,and levels of interest in those topics, for audiences (rather thanindividuals). The audiences are defined by custom trade zones.

The present interest level method of targeting is more accurate thandemographic profiling because the method involves working directly withinterest data, and the ensuing responsiveness of audiences to relevantmessaging, rather than drawing inferences about interests fromdemographic data.

The present interest level targeting has advantages over contextualtargeting in that contextual targeting is static and presumes aninterest based on a single point of contact between a viewer and contentat a point in time. The present interest level targeting involves muchdeeper data sets which depict comparative levels of interest. Theinterest level data leverages both multiple contact points between anaudience and content, and the trend lines over periods of time. As aresult, targeting does not have to be connected to specific content toreach the right viewers, which opens the possibility (and can reduce thecost) of acquiring media to reach the right audiences.

Behavioral targeting relies on tracking individual web consumption thatlimits targeting to viewers who have left a data trail online—and whohave not deleted the cookies from their computers. An inherentdisadvantage is that retargeting volume is limited to a subset of onlineviewers, and the value of cookies can be diluted as they may be brokeredand resold several times to competing advertisers. The present interestlevel targeting can be used to reach entire audiences, without beinglimited to subsets of those audiences.

Some portions of the detailed descriptions that follow are presented interms of algorithms and symbolic representations of operations on databits within a computer memory. These algorithmic descriptions andrepresentations are the mechanisms used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. A method is here, and generally, conceivedto be a self-consistent process leading to a desired result. The processinvolves physical manipulations of physical quantities. Usually, thoughnot necessarily, these quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared, and otherwise manipulated. It has proven convenient at times,principally for reasons of common usage, to refer to these signals asbits, values, elements, symbols, characters, terms, numbers, or thelike.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or the like, refer to the action andprocesses of a computer system, or similar electronic computing device,that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

The present method and system also relates to apparatus for performingthe operations herein. This apparatus may be specially constructed forthe required purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a computerreadable storage medium, such as, but is not limited to, any type ofdisk including floppy disks, optical disks, CD-ROMs, andmagnetic-optical disks, read-only memories (“ROMs”), random accessmemories (“RAMs”), EPROMs, EEPROMs, magnetic or optical cards, or anytype of media suitable for storing electronic instructions, and eachcoupled to a computer system bus.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general-purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the required method steps. The required structurefor a variety of these systems will appear from the description below.In addition, the present invention is not described with reference toany particular programming language. It will be appreciated that avariety of programming languages may be used to implement the teachingsof the method and system as described herein.

FIG. 1 illustrates an exemplary computer architecture for use with thepresent system, according to one embodiment. One embodiment ofarchitecture 100 comprises a system bus 120 for communicatinginformation, and a processor 110 coupled to bus 120 for processinginformation. Architecture 100 further comprises a random access memory(RAM) or other dynamic storage device 125 (referred to herein as mainmemory), coupled to bus 120 for storing information and instructions tobe executed by processor 110. Main memory 125 also may be used forstoring temporary variables or other intermediate information duringexecution of instructions by processor 110. Architecture 100 also mayinclude a read only memory (ROM) and/or other static storage device 126coupled to bus 120 for storing static information and instructions usedby processor 110.

A data storage device 125 such as a magnetic disk or optical disc andits corresponding drive may also be coupled to computer system 100 forstoring information and instructions. Architecture 100 can also becoupled to a second I/O bus 150 via an I/O interface 130. A plurality ofI/O devices may be coupled to I/O bus 150, including a display device143, an input device (e.g., an alphanumeric input device 142 and/or acursor control device 141).

The communication device 140 allows for access to other computers(servers or clients) via a network. The communication device 140 maycomprise one or more modems, network interface cards, wireless networkinterfaces or other well known interface devices, such as those used forcoupling to Ethernet, token ring, or other types of networks.

FIG. 2A illustrates an exemplary system level architecture for use withthe present system, according to one embodiment. A client device 201having a browser 202 can view a webpage 204 hosted by a server 208. Theserver 208 is in communication with a network 203, the client 201 is incommunication with the network, and one or more ad systems 205 are incommunication with the network 203. The client device 201 cancommunicate with an RTB (real-time bidding) system 206, and the RTBsystem (or systems) 206 are in communication with the network. The RTBsystem (or systems) 206 is in communication with one or more RTB bidders207.

FIG. 2B illustrates an exemplary auction process for use with thepresent system. An RTB system calls one or more RTB bidders 209 to holdan auction for each advertisement impression. Bidders respond 210 withina set period of time, for example 10-100 ms depending on the system andnetwork latency between the servers. A bidder is selected 211. Accordingto one embodiment, the highest, and usually the fastest, bidder wins theauction. The winning bidder's display instructions are returned to theclient device 212 and processed 213 to display an ad 214. As an example,the display is rendered by returning HTML or JavaScript that calls to anad system(s) to render the final display. Multiple ad systems may becalled with the final ad system displaying the ad. The ad system tracksmetrics about the ad and performance, according to one embodiment.

FIG. 3 illustrates an exemplary provider system layout for use with thepresent system, according to one embodiment. An analytics processingsystem 301 runs any number of algorithms using data from a bidder system302 and/or ad system 303 and/or data sets associated by (trade zone)shapes. The output of the algorithms are configurations that are loadedinto the bidder system 302 and ad system 303 to execute when performingbidding and ad decision processing. The ad system 303 can providefurther refinement of campaign selection and/or serve the ad. The adserver may defer to another ad server to display the ad after thedecision is made if needed. Ad metrics are tracked by these systems,according to one embodiment. Examples of ad metrics include but are notlimited to impressions delivered, clicks, and price per impression.

According to one embodiment, configurations are constructed by theanalytics processing system 301. During construction of theconfiguration for one or more campaigns, all data sets and the IPaddress space are mapped to custom defined geographic shapes. Theseshapes are not confined to any external pre-existing geographic shapesand can therefore be constructed to best meet the system needs and/orcampaign performance optimization. According to one embodiment, areasare analyzed on one or more sets of data parameters and similar areasare combined into a shape. Trade zones are constructed from smallershapes that are too small to realistically represent an IP range mappingdue to the nature of IP distribution. When aggregating smaller shapes toconstruct the trade zones, consideration includes but is not limited tothe geographic areas that best conform to the probable distribution ofthe IP address ranges, the demographic homogeneity of the individualshapes that are aggregated to form the trade zone, natural geographicboundaries that can create segmentations for targeting, types of IPaddress ranges including, by way of example, business, residential, orpublic location assigned to the IP addresses, and any other factors thatcan be used to identify similarities between the individual shapes.Combining the smaller shapes into larger shapes creates a targetingshape. This delivers a more realistic view of both the areas reached bythe assigned IP addresses and the probable audience within theconstructed shape.

One example of trade zone construction is to use census tracts or blockgroups as the “building blocks” to create the trade zone shapes. UsingIP location data, in combination with but not limited to businesslocation data, IP ranges can be mapped into one of the smaller shapes ora primary shape. The surrounding shapes without direct IP mappings areassigned to this primary shape using both proximity factors and otherdata sets (such as demographic data encompassing income, educationlevel). Assigning the other shapes to the primary shape creates thegroupings of individual shapes that become the trade zone shape.Multiple data sets may be used to create decision criteria for groupingthe shapes. The shapes are contiguous to accurately capture the possibledistribution of the IP addresses. Once the trade zone shapes areconstructed, IP mappings can be updated frequently when new IP, businesslocation and/or other data sets become available.

An unlimited number of datasets can be mapped into the shapes usingspatial relationships to establish an unlimited number of distinguishingcharacteristics about the shapes. The shapes also have a mapping to IPranges that allow targeting to be executed by the targeting systems suchas but not limited to publisher, bidding and ad targeting systems. Someor all of the datasets can be used in the algorithms that producetargeting configurations. Each new dataset provides another set ofparameters that can be used in determining a configuration.

The custom groupings and shapes are more effective than commonly usedexternal static definitions of geographic areas because they are nottied to any one data set's definition and can be constructed to betterapproximate true online groupings based on shared characteristics suchas demographic profile, IP address distribution or consumption patterns.They can also be changed and/or updated as needed. These shapes allowfor an unlimited number of data sets to be added to a shape mapping andused in combination. Datasets can be added to the system at anytime.

According to one embodiment, the shape-based IP mappings are enhancedbased on other dimensions. These additional dimensions are added on topof a base mapping that already incorporates shapes. An example of anadditional dimension is type of IP address. Examples of types of IPaddresses include but are not limited to business, residential, andpublic.

According to one embodiment, the final output is a data profile of thedesired audience. The data profile is mapped to the shapes which arethen mapped to IP groups such that when there is a potential match withan IP address in the configuration, the targeting systems such as butnot limited to publisher, bidding and ad targeting systems can targetthe request.

According to one embodiment, the system finds impression matches thatare a good fit by projecting across the data sets to shapes that havesimilar profiles for the data sets or parameters being used. Thisapplied decision criteria is independent of specific geography. As anexample, a data set may identify a shape in Ames, Iowa that has asimilar profile to a shape in a Santa Fe, N. Mex. over the sameparameter set, so both of the shapes and thus their IP ranges can beincluded in the configuration for targeting. As another example, duringan ad campaign trade zones that are determined to be responsive to aparticular advertising message can then be compared to other trade zonesthat contain similar combinations of characteristics. These similar or“look-alike” trade zones can then also be targeted by the ad campaign.

According to one embodiment, the present matching process allowstargeting systems decisions on individual requests to be made based onwhether or not the operator of a browser—in a probabilistic sense—islikely to share the characteristics of the desired audience profile.Using a probabilistic approach enables configurations for targeting thatencompass a wider range of audience than traditional systems have.Decisions are made on requests that look enough like the desired dataprofile, rather than definitively belonging to a specific data set (i.e.a cookie-based decision). This look-alike method allows the system totarget requests in volumes that can support campaigns and still maintainperformance by focusing the targeting on the desired audience.

According to one embodiment, additional filtering is based on contextualinformation, in addition to the basic profile information. For example,an IP address may map to the desired profile, but the impression on apublisher site, or page within a publisher site, that is not desired fora specific target item.

According to one embodiment, the present system improves advertisement(herein referred to as “ad”) performance through a shape-based targetingsystem that acquires pre-targeted impressions and handles multiplesimultaneous campaigns. The present system matches, bids on, andacquires ad impressions on an individual basis and in real-time, using ashape based targeting system. Therefore the present system enables adcampaigns to achieve improved performance by selectively acquiringimpressions that, in a probabilistic sense, will reach only the desiredaudience for a specific campaign. Since the impression is pre-targetedto at least one campaign, wasted impressions are minimized. Improvedperformance is achieved without having to collect, analyze, and basetargeting criteria on individual online user habits. An individualonline user does not have to have been “seen” by the present system inorder for the system to be effective in determining whether or not totarget that user.

According to one embodiment, bid price is determined by the highestremaining campaign price after all filtering is completed. The price acampaign will pay is determined per campaign per page along with othercharacteristics of the impression such as size or placement on the page.If multiple campaigns overlap, or can be shown to a single impression,the bidders and/or ad systems can select the final campaign for theimpression by using a selection algorithm to complete the selection.Examples of selection algorithms include but are not limited toweighted, random, and sequential. The ad serving system can also use thewinning price as an input to its algorithm. In this way, multiplecampaigns can be run on the bidding/ad server platforms simultaneously.

FIG. 4 illustrates an exemplary real-time bidding (RTB) bidder systemfor use with the present system, according to one embodiment. A thirdparty bidding system 401 is in communication with a bidder 402, and thebidder 402 is in communication with a configuration server 403. It isnoted that a bidding system 401 can be in communication with multiplebidders 402, and a bidder 402 can be in communication with multipleconfiguration servers 403. A third party bidding system 401 is alsoreferred to as an “advertising exchange.” Third party bidding systems401 offer to sell impressions for publishers by enlisting providercompanies to implement bidders 402 to bid on impression requests ontheir systems. A configuration server 403, according to one embodiment,is a system that contains configuration information about campaigns tobe used by the bidders 402 for impression purchase decisions. To makelookups over large amounts of data (millions of rows per campaign)faster, the configuration servers 403 keep all of the configurationsloaded in memory, organized in a way to provide optimal lookup. As anexample, a database such as MySQL can be configured to keep all thetables in memory. As another example, a custom cache server can bewritten to keep all of the data in memory in a manner that is optimizedfor fast lookups. According to one embodiment, multiple configurationservers 403 that either duplicate and/or partition the data by campaignexist.

FIG. 5 illustrates an exemplary bidder for use with the present system,according to one embodiment. A bidder 506 is in communication with oneor more configuration servers 505. A bidder 506 processes requests tobid on impressions from a third party bidding system 401 to determine ifit should bid on the impression based on its current configurations forrunning campaigns. Bidders 506 support multiple bidding API's because ofthe API module layer. A bidder has multiple API modules 501, 502, 503 inthe API module layer. The API module layer is able to translate arequest from a specific API into a software structure that the coresystem can use, and then translate the response back into the specificAPI format. To support a new bidding API, only a new API Module needs tobe added to the bidder 506.

The bidder 506 uses core bidding logic 504 for bidder processing. Duringbidder 506 processing, a bid request is sent to the bidder 506. Thebidder 506 filters it against mechanical constraints, including but notlimited to size, creative type, and allowed destinations. The bidder 506filters remaining ads against current configurations. Finally, a bid isexecuted if the result set of this second filter is not empty.

FIG. 6 illustrates an exemplary advertisement system for use with thepresent system, according to one embodiment. According to oneembodiment, an ad system can work independently or in conjunction withthe RTB systems. The ad system uses the campaign configurationinformation to target and select a campaign ad to display to fill animpression request 602. An ad system 601 is in communication with one ormore configuration servers 603. A request to fill an impression 602 isreceived by the ad system 601. The ad system 601 filters campaigns bymechanical constraints, examples include but are not limited to size andbrowser. The ad system 601 filters remaining ads against currentcampaign configurations. An ad is selected from the result set, and thead is served to a client by an ad server that contains the ad.

FIG. 7 illustrates an exemplary configuration process for use with thepresent system, according to one embodiment. A profile parameter set isgenerated 701 via manual or automated mechanisms. Manual mechanisms caninclude selection by a customer, and automated mechanisms can includefeedback based mechanisms. The profile parameter set is used to matchthe profile to shapes (trade zones) and/or domains/pages 702. Theselected shapes (trade zones) are projected online via shape to an IPaddress mapping 703. The selected domains/pages create a rule that isadded to the configuration. The configuration of IP addresses anddomains/pages is complete 704 and the configuration targets the desiredaudience for a targeting item. The configuration is loaded intoconfiguration servers 705 for targeting systems to use for targeting. Anexample of matching profile to shapes 702 is illustrated below in FIG.9B.

FIG. 8A illustrates an exemplary bidding process for use with thepresent system, according to one embodiment. A bid request is received801 and the bid request is translated from API specific to an internalformat 802. A possible ad set is created 803 and the ad set is filteredagainst the bid request 804. The filtering of the ad set is performedusing mechanical constraints such as size, creative, type, and allowedsites, according to one embodiment. The remaining ads in the set aretargeted 805 using the IP address and configuration servers. The ad setis filtered using targeting information 806. Price is selected 807 fromremaining ads in the set and a bid is made (if empty set, pass). The bidresponse is translated from internal format to API specific format 808,and the request is directed to the ad system 809 upon winning the bid.The ad system selects the final ad and returns display instructions forthe ad to the client 810.

FIG. 8B illustrates an exemplary publisher targeting process for usewith the present system, according to one embodiment. A websitepublisher sends a targeting request 811 and the request is translatedfrom a publisher specific format to an internal format 812. A possibletarget set is created 813 and the set can be filtered against the targetrequest parameters 814. The filtering of the target set is performedusing mechanical constraints such as size, creative types, or otherconstraints imposed by the publisher, according to one embodiment. Theremaining target items in the set are targeted 815 using the IP addressand configuration servers which contain targeting configurations. Theset is filtered using targeting information 816. The final target isselected 817 and the corresponding publisher specific targetinginformation is translated from an internal format to the publisherspecific format 818. The request is returned to the publisher and thepublisher uses the targeting response data to perform a target action819. Items on a publisher web page that are targeted include but are notlimited to content display, ads to display, and coupons to display.

According to one embodiment, when there is a match with multiple targetitems to a trade zone, the best fit target item is selected by givingpriority to fit with high interest levels and balancing the distributedrequests across all target items that have a fit.

According to one embodiment, a targeting configuration is developed foreach targeting item (such as but not limited to an ad campaign, bidderitem, ad selection, publisher target action) to integrate the item intothe targeting systems. The configuration is used to identify therequests that will be targeted to the selected custom trade zones. Thetargeting systems evaluate each individual request to determine whetherthere is a fit with any target items in the decision set. Response datais evaluated during the lifetime of the targeting item (examples ofresponse data include but are not limited to click-through rates, coupondownloads, impressions delivered, price per impression, leadsubmissions, landing page arrivals, product queries, and productpurchases) to refine the targeting criteria.

FIG. 9A illustrates an exemplary trade zone level interest determinationprocess for use with the present system, according to one embodiment.Content consumed by users in a trade zone across time is collected inseveral ways. One embodiment creates an inventory of consumed contentfrom several sources 901. Example inventory or content sources includeyet are not limited to web pages from websites providing data feeds, webpages associated with target requests, purchased website server logs,mobile applications, television programming transcripts (and other videosources), and on-demand video selections. In one embodiment, thiscontent is stored 902 allowing for further uses without impeding thereal-time content collection. As an example, web pages from a websiteproviding a page visit data feed via pixel image requests 901 are stored902 and have associated raw content 903 collected. Once raw content iscollected, it is tokenized 904 while preserving contextual metadata.Contextual metadata changes depending on the content source. Forexample, web page token location and formatting characteristics can bepreserved. Video transcript metadata can include demographic informationabout the speaker or scene descriptions, for example. The strippedcontent (e.g. tokens) is associated with original source data 905 andstored in the data storage system 902. Tokenization occurs at the word,word pair, phrase or other level, for example.

FIG. 9B illustrates an exemplary trade zone level interest determinationprocess for use with the present system, according to one embodiment. Inone embodiment, statistics are collected for the token of choice andaggregated at the trade zone level 906. An exemplary implementation of atargeting system calculates a consumption rate of a particular word ortoken for a trade zone, by multiplying the number of times the contentwas consumed by the number of occurrences of a chosen token in thecontent. As an example, if the content is a webpage, the number ofoccurrences of a token is summed across all pages viewed by the tradezone to result in the consumption rate of the token by the trade zone906. Additional statistics such as the count of unique consumers of atoken in a trade zone, the count of unique content (for example numberof unique pages) containing the token, and various statistics describingthe distribution of each token and source content in a trade zone can becollected and used in a similar fashion.

According to one embodiment, collections of tokens are analyzed 913 todiscover distinct or relative interests for each trade zone. Therevealed interest data is mapped to custom trade zones to organize theinterest level information in a way that can be used effectively fortargeting. As an example, trade zones consuming more words associatedwith world cup soccer will index higher for that interest. The revealedinterests or behaviors are mapped to targetable units (trade zones) andlinked to targeting actions. In one embodiment, the data also indicatesintensity for a given product or targeting objective by comparing thenumber of unique consumers of an interest in a trade zone to the volumeof consumption.

According to one embodiment, collections of tokens are compared acrosstime intervals to determine trends 914. In one embodiment, trends aredetermined by monitoring the frequency of tokens consumed over a timeperiod, and can be an upward or a downward trend. Aggregation occurs atvarious time intervals to reveal microlevel and macrolevel trends. Oncediscovered 914, trends are mapped to trade zones 913 to be used as atargeting configuration for campaigns 915 or used to discover new SEM(search engine marketing) keywords for clients 907.

According to one embodiment, interests are matched to current andpotential campaigns to optimize performance. One embodiment uses tokensthat describe a particular campaign's objective 909. The tokens act asthe seed 910 to discover which trade zones are more interested in thecampaign's objective. As an example, only trade zones with a highinterest in environmentally friendly products surrounding a particularstore will be matched to an ad campaign for that store highlightinggreen products on sale. Another approach is to use a client's SEMkeywords 908 as the seed for interest discovery. As an example, adepartment store campaign might optimize on terms such as fashion andvarious high-end designers. Trade zones that have a higher interest infashion and these designers will be matched to the department store adcampaign, rather than advertising to all areas around the store.

According to one embodiment, linear and nonlinear classificationmethods, supervised and unsupervised learning algorithms, neuralnetworks and various multidimensional data mining techniques are used913 to reveal trends and interests, without a seed token vector. Theseorganic insights are mapped into new keywords and phrases that indicateinterest in a product 907. These insights are shared with existing andpotential clients to enhance SEM 908 and/or improve campaignperformance. Also, supplied and discovered tokens are expanded 910 usingwell-known token similarity algorithms. In one embodiment, discoveredinterests define virtual categories 907. For page content, virtualcategories use tokens from successful impressions as a seed to comparefuture impression content. Scores from this process are used to optimizetrade zone 915 and/or page selection 916, as one example.

According to one embodiment, tokenized data from web pages remainsassociated with pages. Term weighting schemes 911 are used todynamically assign page scores 912 based on the discovered interests 907or virtual categories, client SEM list 908, or campaign keywords 909.Rather than have pages scored by static categories, scoring criteria isoptimized to achieve an individual campaign's objectives for allconcurrent campaigns, regardless of commonality. These pages are thenused as input to a campaign's targeting configuration 916.

A system for improving shape-based targeting by using interest leveldata has been disclosed. It is understood that the embodiments describedherein are for the purpose of elucidation and should not be consideredlimiting the subject matter of the disclosure. Various modifications,uses, substitutions, combinations, improvements, methods of productionswithout departing from the scope or spirit of the present inventionwould be evident to a person skilled in the art.

1. A computer-implemented method, comprising: creating one or more tradezones, wherein creating a trade zone comprises grouping a set ofparameters to deliver custom shapes; clustering the custom shapesaccording to offline data, online data, and geographic distribution ofIP addresses; and mapping clusters of the custom shapes to IP addresses;receiving data indicating consumption of a content source by one or moretrade zones at a calculated rate; analyzing the calculated rate todetermine an interest associated with each trade zone; selecting the oneor more trade zones based upon a desired interest representative of adesired audience; transmitting a targeting request to the one or moreselected trade zones including display instructions and informationassociated with a target action; and performing the target action. 2.The computer-implemented method of claim 1, wherein online data andoffline data associated with the one or more trade zones are independentof cookies, and wherein the custom shapes are independent of geographiclocation.
 3. (canceled)
 4. The computer-implemented method of claim 1,wherein offline data comprises demographic data, point of sale data, andbusiness information data, and online data comprises pane content, userclicks, advertisement impressions, and pages visited.
 5. (canceled) 6.The computer-implemented method of claim 1, wherein offline data useslocation information in the offline data to map to the custom shapes,and online data uses IP addresses to map to the custom shapes. 7.(canceled)
 8. The computer-implemented method of claim 1, whereintransmitting a targeting request to the one or more selected trade zonesresults in properly targeted actions, wherein properly targeted actionsreach only the desired audience.
 9. The computer-implemented method ofclaim 1, wherein a trend of an interest for a trade zone is calculated,wherein the trend is one of upward or downward, and further comprisingat least one of mapping the trend to the one or more trade zones to useas a targeting configuration for campaigns and discovering new searchengine marketing keywords using the trend.
 10. The computer-implementedmethod of claim 1, wherein the content source is one of a webpage, apurchased server log, a mobile application, a television programmingtranscript, or an on-demand video selection.
 11. Thecomputer-implemented method of claim 1, wherein consumption of thecontent source comprises: organizing one or more keywords according tothe one or more trade zones, wherein the one or more trade zones consumethe one or more keywords at a calculated rate determined by multiplyinga first number of times that a content source was viewed by a secondnumber of occurrences of a chosen keyword in the content source. 12.(canceled)
 13. The computer-implemented method of claim 1, whereinconsumption of the content source at a calculated rate comprises: usinga data mining technique to produce an output, wherein the output ismapped into one or more phrases having one or more words; and organizingthe one or more phrases according to the one or more trade zones,wherein the calculated rate is determined by multiplying a first numberof times a content source was viewed by a second number of occurrencesof a chosen phrase in the content source, and wherein data miningtechniques comprise linear and nonlinear classification methods,supervised and unsupervised learning algorithms and neural networks. 14.(canceled)
 15. (canceled)
 16. The computer-implemented method of claim1, wherein creating a trade zone further comprises combining customshapes that are too small to represent an IP range mapping based onconsideration of one or more sets of parameters comprising geographicareas that best conform to the probable distribution of the IP addressrange, demographic homogeneity, natural geographic boundaries and typesof IP address ranges, wherein types of IP address ranges comprisebusiness, residential and public locations.
 17. (canceled) 18.(canceled)
 19. The computer-implemented method of claim 1, whereinselecting one or more trade zones is further based on using probabilityto find a range of interests that related to a desired interest of adesired audience.
 20. The computer-implemented method of claim 1,wherein transmitting a targeting request to the one or more selectedtrade zones further comprises transmitting simultaneously to similartrade zones based on one or more sets of parameters.
 21. Thecomputer-implemented method of claim 1, transmitting a targeting requestto the one or more selected trade zones further comprises using aselection process to select the best target action, wherein theselection process comprises weighted, random and sequential processes.22. (canceled)
 23. The computer-implemented method of claim 1, furthercomprising optimizing the targeting request based upon performancemetrics collected when performing the target action, wherein performancemetrics comprise click-through rates, coupon downloads, impressionsdelivered, price per impression, lead submissions, landing pagearrivals, product queries and product purchases.
 24. (canceled)
 25. Thecomputer-implemented method of claim 1, wherein information associatedwith a target action comprises a weighted score dynamically assigned toeach content source based upon one or more desired interests.
 26. Thecomputer-implemented method of claim 1, wherein creating a trade zonefurther comprises assigning one or more secondary custom shapes based oncensus tracts that are in close proximity to the clusters of customshapes.
 27. The computer-implemented method of claim 1, whereinperforming the target action occurs independent of keywords and a aprior online presence of the desired audience.
 28. Thecomputer-implemented method of claim 1, further comprising: determiningwhether multiple target items match the one or more selected tradezones; and determining a best fit target item of the multiple targetitems.
 29. (canceled)
 30. (canceled)
 31. (canceled)
 32. A system,comprising: a server, hosting a first webpage, in communication with anetwork, wherein a client system accesses the first webpage via thenetwork; and a targeting system in communication with the network,wherein the targeting system creates one or more trade zones by groupinga set of parameters to deliver custom shapes; clustering the customshapes according to offline data, online data, and geographicdistribution of IP addresses; and mapping clusters of the custom shapesto IP addresses; receives data indicating consumption of a contentsource by one or more trade zones at a calculated rate; analyzes thecalculated rate to determine an interest associated with each tradezone; selects the one or more trade zones based upon a desired interestrepresentative of a desired audience; transmits a targeting request tothe one or more selected trade zones including display instructions andinformation associated with a target action; and performs the targetaction.
 33. (canceled)
 34. (canceled)
 35. (canceled)
 36. (canceled) 37.(canceled)
 38. (canceled)
 39. (canceled)
 40. (canceled)
 41. (canceled)42. (canceled)
 43. (canceled)
 44. (canceled)
 45. (canceled) 46.(canceled)
 47. (canceled)
 48. (canceled)
 49. (canceled)
 50. (canceled)51. (canceled)
 52. (canceled)
 53. (canceled)
 54. (canceled) 55.(canceled)
 56. (canceled)
 57. (canceled)
 58. The system of claim 32,further comprising a real-time bidding system and a plurality ofreal-time bidders in communication with the network, wherein thetargeting system acquires impressions from the real-time bidders forserving advertisements, and a publisher site in communication with thenetwork, wherein the publisher site uses the targeting system to performtarget actions for the publisher site's audiences.
 59. (canceled) 60.(canceled)
 61. (canceled)
 62. (canceled)
 63. (canceled)
 64. (canceled)65. (canceled)